> ## Documentation Index
> Fetch the complete documentation index at: https://docs.domino.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Use Cohort Analysis
When the Model Monitor detects data drift or a reduction in quality for a regression model, the Cohort Analysis gives you details so you can take remedial action. It identifies underperforming cohorts of data, determines what differentiates this data, and shows you specific features of the data that require examination. See [Set up Cohort Analysis](/cloud/platform-capabilities/features/monitoring/4-set-up-model-monitor/6-set-up-cohort-analysis-mm) for information about how to enable this feature.
This feature is only available for regression models with numerical features.
## Review the Cohort Analysis
You can access the job or report for the Cohort Analysis from the Model Monitor.
* The job gives you access to the JSON files that contain the code used to perform the analysis, as well as the cohort statistics. You can copy the code and statistics to create custom reports or to use when remediating your model.
* The report shows a detailed cohort analysis. Use this to drill down into each cohort to understand the primary features that impact the model’s performance.
Review the Cohort Analysis:
1. From the navigation pane, click **Model Monitor**.
2. Select the model for which you generated the Cohort Analysis.
3. Click **Model Quality**.
4. In the Cohort Analysis Report area, click one of the following:
* **Go to Job** to see the job. Click the **Results** tab to see the JSON data that was used to generate the report. In the navigation pane, you can click **Files** to see the related files.
* **View Report** to open a Files page that has the .pdf. Click `.pdf` to view the report. The report has the following sections:
1. Section 1 shows a visual representation and summary statistics about the model quality for the worst-performing cohorts. You can investigate these cohorts.
2. Section 2 shows information about the most distinctive features (quantified by a contrast score) for the top worst-performing cohorts. You can investigate a feature or a combination of features for remediation.
3. Section 3 shows summary statistics for all cohorts listed in the order of model performance. It also lists the features for a cohort that contribute to the cohort’s distinctiveness.
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